Relational and non-relational databases are popular tools for organizing data and for retrieving data for subsequent review and analysis. These databases frequently handle large amounts of related or disparate data, and can be deployed to support a variety of applications.
However, the data structures generally employed in databases are designed to access data infrequently, because this process consumes time and system resources and may create a “bottleneck” for system performance. This is a particular concern for relational databases, which may involve large amounts of data and data structures, and require complex coding to present database calls or otherwise query the database, as well as to communicate with user applications.
Due to the complexity of the data and the required coding, a large investment of time and labor resources may be necessary to create a functional database, as well as to validate the data and ensure the coding is operating properly. Another consequence of this size and complexity is that the database and the associated application and coding may become unwieldy, and specialized coding with diminished portability may be necessary to query a particular database in an efficient manner.
Accordingly, there is a need to improve the operation of databases and processes for querying a database, to reduce the demand on system resources and provide cost-effective database functionality for a wide variety of applications.